Bibliographic record
Abstract
Bent crystals have demonstrated potential for use in beam collimation. A process called channeling is when accelerated particle beams are trapped by the nuclear potentials in the atomic planes within a crystal lattice. If the crystal is bent then the particles can follow the bending angle of the crystal. There are several different effects that are observed when particles travel through a bent crystal including dechanneling, volume capture, volume reflection and channeling. With a crystal placed at the edge of a particle beam, part of the fringe of the beam can be deflected away towards a detector or beam dump, thus helping collimate the beam. There is currently FORTRAN code by Igor Yazynin that has been used to model the passage of particles through a bent crystal. Using this code, the effects mentioned were explored for beam energy that would be seen at the Facility for Advanced Accelerator Experimental Tests (FACET) at a range of crystal orientations with respect to the incoming beam. After propagating 5 meters in vacuum space past the crystal the channeled particles were observed to separate from most of the beam with some noise due to dechanneled particles. Progressively smaller bending radii, with corresponding shorter crystal lengths, were compared and it was seen that multiple scattering decreases with the length of the crystal therefore allowing for cleaner detection of the channeled particles. The input beam was then modified and only a portion of the beam sent through the crystal. With the majority of the beam not affected by the crystal, most particles were not deflected and after propagation the channeled particles were seen to be deflected approximately 5mm. After a portion of the beam travels through the crystal, the entire beam was then sent through a quadrupole magnet, which increased the separation of the channeled particles from the remainder of the beam to a distance of around 20mm. A different code, which was developed at SLAC, was used to create an angular profile plot which was compared to what was produced by Yazynin's code for a beam with no multiple scattering. The results were comparable, with volume reflection and channeling effects observed and the range of crystal orientations at which volume reflection is seen was about 1 mrad in both simulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".